We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application. INDEX TERMS Computed tomography, heart segmentation, multi-atlas segmentation, random walk.

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Reef-building corals form complex relationships with a wide range of microbial partners, including symbiotic algae in the family Symbiodiniaceae and various bacteria. These coral-associated communities can be shaped to varying degrees by environmental context. Sedimentation can structure a coral’s microbial community by altering light availability for symbiotic algae, triggering the coral’s stress response, or serving as a reservoir for both pathogenic and essential bacterial and algal symbionts. To examine the influence of sedimentation on a coral’s microbiome, we used 16S rDNA and ITS-2 amplicon sequencing to characterize the bacterial and algal communities associated with the massive scleractinian coral Porites lobata across pairs of sites along a naturally occurring sedimentation gradient in Fouha Bay, southern Guam. Additionally, we investigate the influence of proximity to sediment on the coral colony scale, by sampling from the edge and center of colonies as well as the nearby sediment. The P. lobata colonies associated with several different genotypes of Cladocopium C15 algal symbionts and often harbored different genotypes within a single colony. However, the different Cladocopium genotypes showed no structuring according to colony position or location along the sedimentation gradient. Bacterial communities were largely consistent across the sedimentation gradient, however, some rarer taxa were differentially abundant across sites. Planococcaceae shows higher abundance closer to the river mouth in coral colonies in both the edge and center of colonies. Peredibacter also shows high abundance near the river mouth but only in sediment and the edges of the colony. We find sediment plays a larger role structuring bacterial communities at the colony scale compared to a coral’s position along the sedimentation gradient. Edge communities look more similar to the sediment compared to the center communities and are also enriched in similar pathways such as those involved in nitrogen fixation. We also find center samples to be dominated by Endozoicomonas compared to the edge, supporting a role for this taxon in structuring bacterial communities and limiting bacterial diversity in coral colonies. Together these results show the differential impact sedimentation can have between sections of the coral colony microhabitat.

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This paper utilizes a synchronized Lorenz chaotic drive/response system, which uses Haar filtering and appropriate thresholding in order to detect a transmitted random binary message. Using the Lorenz chaotic attractor to obscure the message, the transmission is passed through an Additive White Gaussian (AWG) channel to successfully retrieve the original binary random data. The detection mechanism employs the Haar Wavelet Transform in combating the channel noise. A communication technique using Chaotic Parameter Modulation (CPM) is simulated in Matlab and prototyped on a reconfigurable hardware platform from Xilinx.